Computer Vision Applications and Learning Path: Popular Technologies, Practical Tools, and Career Development Guide

Computer Vision (CV), as an important branch of the field of artificial intelligence, has developed rapidly in recent years. This article aims to sort out the popular technical directions in the current computer vision field, recommend practical tools, and provide learning paths and career development suggestions to help readers quickly get started and deeply understand this field.

I. Scanning of Popular Technical Directions

According to the "Three Hot Topics" released at CVPR (Conference on Computer Vision and Pattern Recognition) and discussions on X/Twitter, the current popular directions in the field of computer vision include:

1. 3D from Multi-View and Sensors: Reconstructing three-dimensional scenes using multiple images or sensor data (such as LiDAR, depth cameras). This technology has a wide range of applications in autonomous driving, robot navigation, virtual reality, augmented reality, and other fields.

2. Image and Video Synthesis: Generating realistic image and video content using generative adversarial networks (GANs), diffusion models, and other technologies. This technology has great potential in game development, film special effects, advertising production, and other fields. For example, tools such as Stable Diffusion and DALL-E can generate high-quality images.

3. Multimodal Learning, and Vision, Language, and Reasoning: Combining visual information with language information to enable computers to understand the content of images or videos and perform reasoning and decision-making. This technology has a wide range of applications in intelligent customer service, autonomous driving, image description, visual question answering, and other fields. For example, the LIBERO-X paper studies the robustness of visual-language-action models.

In addition to the above three major directions, the following technologies are also worth paying attention to:

• Object Detection: Identifying and locating specific objects in images or videos. The YOLO series algorithms (YOLOv3, YOLOv5, YOLOv8) are among the currently popular object detection algorithms.
• Image Segmentation: Dividing an image into different regions, each representing a semantic object. U-Net is a network structure commonly used for medical image segmentation.
• OCR (Optical Character Recognition): Recognizing text in images. It is widely used in document digitization, license plate recognition, text translation, and other fields.
• Robotics Vision: Applying computer vision technology to robot control and navigation. For example, the Delft University of Technology's drone racing team uses end-to-end neural networks to directly control drone movement from pixel input, without the need for traditional Kalman filters or feature detectors.
• Medical Imaging: Using computer vision technology for medical image analysis to assist doctors in diagnosis and treatment.
• Autonomous Vehicles: Using computer vision technology to identify traffic signs, pedestrians, vehicles, etc., to achieve autonomous driving functions. Related papers also focus on safety and attack vectors in autonomous driving environments.
• Vision-Language Models: Combining visual information and text information to achieve tasks such as image description generation and visual question answering.

II. Recommended Practical Tools

The following are some commonly used tools in the computer vision development process:

1. Development Frameworks:

   • PyTorch: A deep learning framework developed by Facebook (Meta), widely popular for its flexibility and ease of use. KirkDBorne recommended a series of PyTorch tutorials, suitable for beginners to get started with computer vision.
   • TensorFlow: A deep learning framework developed by Google, with a strong ecosystem and rich resources.
   • MATLAB: Commercial mathematical software developed by MathWorks, providing rich computer vision toolboxes and examples. MATLAB officially provides more than 50 computer vision examples, including code, for easy learning and application.

2. Data Annotation and Management:* Roboflow: A platform that provides data annotation, model training, and deployment functions. The NPC project of @@measure_plan used Roboflow's rf-detr segmentation model.

• Labelbox: An enterprise-level data annotation platform that provides powerful team collaboration and data management functions.

3. Other tools:

   • Mediapipe: A cross-platform machine learning framework developed by Google that provides functions such as face detection and human pose estimation. The NPC project of @@measure_plan also used Mediapipe.
   • Depth of Field Simulator: An open-source depth of field simulator that can help understand and visualize depth of field effects, which is very helpful for controlling image diversity during data acquisition.

3. Learning Path Recommendations

Here is a step-by-step computer vision learning path:

1. Basic knowledge:

   • Linear Algebra: Vectors, matrices, matrix operations, etc.
   • Calculus: Derivatives, gradients, chain rule, etc.
   • Probability and Statistics: Probability distribution, expectation, variance, maximum likelihood estimation, etc.
   • Python Programming: Master the basic syntax and commonly used libraries of the Python language (such as NumPy, Pandas).

2. Deep Learning Basics:

   • Neural Networks: Understand the basic structure and principles of neural networks, such as fully connected networks, convolutional neural networks (CNN), recurrent neural networks (RNN), etc.
   • Backpropagation Algorithm: Master the principles and implementation of the backpropagation algorithm.
   • Optimization Algorithms: Understand commonly used optimization algorithms, such as gradient descent, Adam, etc.
   • Loss Functions: Understand commonly used loss functions, such as cross-entropy loss, mean square error loss, etc.

3. Core Concepts of Computer Vision:

   • Image Processing Basics: Image filtering, edge detection, feature extraction, etc.
   • Convolutional Neural Networks (CNN): Understand the structure and principles of CNNs, and their applications in image recognition, object detection, and other fields.
   • Recurrent Neural Networks (RNN) and Long Short-Term Memory Networks (LSTM): Understand the structure and principles of RNNs and LSTMs, and their applications in video analysis, image description, etc.
   • Generative Adversarial Networks (GAN): Understand the structure and principles of GANs, and their applications in image generation, image restoration, etc.

4. Classic Paper Reading:

   • ResNets: Deeply understand the structure and advantages of residual networks.
   • YOLO: Learn the design ideas of the YOLO series of object detection algorithms.
   • DeConv: Understand the application of deconvolution in image segmentation and generation.
   • GAN: Learn the basic principles of generative adversarial networks.
   • U-Net: Understand the application of U-Net in medical image segmentation and other fields.
   • Focal Loss: Learn an effective method to solve the problem of class imbalance in object detection.

5. Project Practice:

   • Kaggle Competitions: Participate in computer vision competitions on Kaggle to accumulate practical experience.
   • Open Source Projects: Participate in open source computer vision projects to learn code specifications and teamwork.
   • Personal Projects: Try to design and implement your own computer vision projects, such as face recognition, object detection, image classification, etc.

4. Career Development Recommendations

1. Career Directions:

   • AI Engineer: Responsible for the development, deployment, and optimization of computer vision algorithms.
   • Machine Learning Researcher: Engaged in the research and innovation of computer vision algorithms.
   • Data Scientist: Use computer vision technology for data analysis and mining.

2. Skills Enhancement:* Focus on a specific field: Based on Ashishllm's suggestion, focus on subfields such as OCR, object detection, image segmentation, and image recognition for in-depth research and experimentation.

• Master common tools: Become proficient in deep learning frameworks such as PyTorch and TensorFlow, as well as computer vision libraries such as OpenCV.
• Continuous learning: Pay attention to the latest research results and technological development trends, and constantly improve your skills.

3. Job Search Suggestions:

• Accumulate project experience: Accumulate practical experience by participating in projects or internships to demonstrate your abilities.
• Prepare for interviews: Familiarize yourself with common computer vision algorithms and interview questions to demonstrate your technical strength.
• Communicate actively: Actively communicate with recruiters to understand job requirements and company culture. @@__iamaf is actively looking for AI/ML related jobs and can refer to his job search direction.

V. SummaryComputer vision is a field full of opportunities and challenges. By mastering the basics, learning core concepts, participating in project practice, and continuously paying attention to the latest technological development trends, you can quickly get started and deeply understand this field, and ultimately achieve success in your career development. Remember Vincent Sitzmann's point: "Vision" is only meaningful as part of a perception-action loop, and traditional computer vision, which maps images to intermediate representations (3D, flow, segmentation...), is about to disappear. This also suggests that future computer vision research may focus more on end-to-end solutions and more intelligent interaction methods.